Abstract
In this work, multiscale decomposition and sparse representation-based multimodal medical image fusion technique is proposed. An efficient denoising technique, feature-preserving regularized Savitzky–Golay filter is applied to obtain noise-free images. The filtered medical images are split into low- and high-pass subbands by non-subsampled shearlet transform (NSST). The sparse coefficient vectors of low-pass subbands are obtained from a pre-learned dictionary, and “max-L1” rule is applied to obtain the fused low-pass subband. However, high-pass subbands are fused using “max-absolute” rule. Lastly, NSST reconstruction is applied to generate the fused multimodal medical image. The non-subsampled contourlet transform, NSST-based fusion using parameter adaptive pulse coupled neural network and phase congruency techniques are also realized for comparative analysis. Multiple experiments on clean and noisy sets are performed for gray and color medical images. The fusion techniques are also tested on infrared–visible image pairs. The visual and quantitative outcomes verify that suggested technique outperforms the state-of-the-art fusion techniques.
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Goyal, S., Singh, V., Rani, A. et al. FPRSGF denoised non-subsampled shearlet transform-based image fusion using sparse representation. SIViP 14, 719–726 (2020). https://doi.org/10.1007/s11760-019-01597-z
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DOI: https://doi.org/10.1007/s11760-019-01597-z